Leading Data Infrastructure Expert

We are seeking an experienced data infrastructure expert to design, build, and maintain scalable data architecture that enables data-driven decision making across our organization.

The ideal candidate will have expertise in building complex data pipelines, architecting data warehouse solutions, and implementing data quality frameworks. They will collaborate with cross-functional teams to understand data requirements and build infrastructure that enables self-service analytics and advanced data exploration.

• Design, build, and maintain scalable data pipelines and ETL/ELT processes to ingest, transform, and deliver data from various sources.
• Architect and optimize data warehouse solutions for efficient storage, retrieval, and processing of large-scale time-series and analytical datasets.
• Implement and maintain data quality frameworks, monitoring systems, and alerting mechanisms to ensure data accuracy, completeness, and reliability.
• Collaborate with Product Managers, Marketing, Finance, and Sales to understand data requirements and build infrastructure that enables self-service analytics and advanced data exploration.
• Optimize database performance, including query optimization, indexing strategies, and capacity planning for both operational and analytical workloads.
• Build and maintain data infrastructure using cloud platforms (AWS, GCP, Azure) and modern data stack tools, ensuring scalability, security, and cost-effectiveness.
• Develop and maintain data documentation, schemas, and governance processes to ensure data discoverability and proper usage across teams.
• Work closely with Engineering teams to implement event tracking, logging, and instrumentation that captures meaningful product and user behavior data.
• Support real-time data processing requirements and streaming analytics use cases, leveraging Timescale's time-series capabilities.
• Champion data engineering best practices, including version control, testing, monitoring, and CI/CD for data pipelines.

• 4+ years of proven experience as a Data Engineer, Analytics Engineer, or similar role, with significant experience building and maintaining production data pipelines.
• Expert proficiency in SQL for complex data transformations, performance optimization, and working with large datasets. Strong PostgreSQL experience is highly preferred.
• Proficiency in Python or another programming language for data pipeline development, automation, and scripting.
• Experience with modern data stack tools such as dbt, Airflow, Dagster, or similar orchestration and transformation frameworks.
• Strong experience with cloud data platforms (AWS Redshift/RDS, Google BigQuery/Cloud SQL, Azure Synapse, or Snowflake) and their associated data services.
• Understanding of data modeling concepts, dimensional modeling, and database design principles for both OLTP and OLAP systems.
• Experience with data visualization and BI tools (Metabase, Tableau, Looker) and building data marts for analytical consumption.
• Strong understanding of data governance, security, and privacy principles, including experience with data lineage and cataloging tools.
• Excellent problem-solving skills with ability to troubleshoot complex data issues, optimize performance bottlenecks, and scale systems efficiently.
• Experience working in agile, cross-functional teams with strong communication skills for collaborating with both technical and non-technical stakeholders.
• Understanding of software engineering best practices including version control, testing, code reviews, and CI/CD pipelines.
• Experience with time-series databases, developer tooling, or data infrastructure products is a significant advantage.
• Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field, or equivalent practical experience.